DocumentCode :
3145342
Title :
Evolving Bidding Strategies Using Self-Adaptation Genetic Algorithm
Author :
Soon, Gan Kim ; Anthony, Patricia ; Teo, Jason ; On, Chin Kim
Author_Institution :
Sch. of Eng. & Inf. Technol., Univ. Malaysia Sabah, Kota Kinabalu, Malaysia
fYear :
2009
fDate :
15-16 May 2009
Firstpage :
222
Lastpage :
225
Abstract :
This paper investigates the application of self-adaptation genetic algorithm on a flexible and configurable heuristic decision making framework that can tackle the problem of bidding across multiple auctions that apply different protocols (English, Vickrey and Dutch) by using an autonomous agent to search for the most effective strategies (offline). Our study shows that self-adaptation genetic algorithm performance is much better than conventional genetic algorithm. An empirical evaluation on the effectiveness of genetic algorithm and self-adaptation genetic algorithm for searching the most effective strategies in the heuristic decision making framework are discussed in this paper.
Keywords :
decision making; electronic commerce; genetic algorithms; mobile agents; autonomous agent; bidding strategies; configurable heuristic decision making; flexible heuristic decision making; online auction; self-adaptation genetic algorithm; Autonomous agents; Decision making; Evolutionary computation; Genetic algorithms; Genetic engineering; Genetic mutations; Information technology; Intelligent agent; Monitoring; Protocols; Bidding Agent; Bidding Strategies; Genetic Algorithm; Online Auction; Self-Adaptation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Ubiquitous Computing and Education, 2009 International Symposium on
Conference_Location :
Chengdu
Print_ISBN :
978-0-7695-3619-4
Type :
conf
DOI :
10.1109/IUCE.2009.108
Filename :
5223188
Link To Document :
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